Radboud University, Donders Centre for Neuroscience, the Netherlands.
Radboud University, Donders Centre for Neuroscience, the Netherlands; Radboud University Medical Center, Donders Centre for Medical Neuroscience, the Netherlands.
J Neurosci Methods. 2021 Feb 15;350:109063. doi: 10.1016/j.jneumeth.2020.109063. Epub 2020 Dec 25.
Electrophysiological recordings contain mixtures of signals from distinct neural sources, impeding a straightforward interpretation of the sensor-level data. This mixing is particularly detrimental when distinct sources resonate in overlapping frequencies. Fortunately, the mixing is linear and instantaneous. Multivariate source separation methods may therefore successfully separate statistical sources, even with overlapping spatial distributions.
We demonstrate a feature-guided multivariate source separation method that is tuned to narrowband frequency content as well as binary condition differences. This method - comparison scanning generalized eigendecomposition, csGED - harnesses the covariance structure of multichannel data to find directions (i.e., eigenvectors) that maximally separate two subsets of data. To drive condition specificity and frequency specificity, our data subsets were taken from different task conditions and narrowband-filtered prior to applying GED.
To validate the method, we simulated MEG data in two conditions with shared noise characteristics and unique signal. csGED outperformed the best sensor at reconstructing the ground truth signals, even in the presence of large amounts of noise. We next applied csGED to a published empirical MEG dataset on visual perception vs. imagery. csGED identified sources in alpha, beta, and gamma bands, and successfully separated distinct networks in the same frequency band.
COMPARISON WITH EXISTING METHOD(S): GED is a flexible feature-guided decomposition method that has previously successfully been applied. Our combined frequency- and condition-tuning is a novel adaptation that extends the power of GED in cognitive electrophysiology.
We demonstrate successful condition-specific source separation by applying csGED to simulated and empirical data.
电生理记录包含来自不同神经源的信号混合物，这阻碍了对传感器级数据的直接解释。当不同的源在重叠频率中产生共鸣时，这种混合尤其有害。幸运的是，混合是线性和瞬时的。因此，即使空间分布有重叠，多变量源分离方法也可以成功地分离统计源。
我们展示了一种特征引导的多变量源分离方法，该方法针对窄带频率内容以及二进制条件差异进行了调整。这种方法 - 比较扫描广义特征分解（csGED） - 利用多通道数据的协方差结构来找到最大限度地分离两个数据子集的方向（即特征向量）。为了驱动条件特异性和频率特异性，我们的数据子集取自不同的任务条件，并在应用 GED 之前进行了窄带滤波。
为了验证该方法，我们在具有共享噪声特征和独特信号的两种条件下模拟了 MEG 数据。即使在存在大量噪声的情况下，csGED 也优于最佳传感器，可重建真实信号。接下来，我们将 csGED 应用于已发表的关于视觉感知与想象的实证 MEG 数据集。csGED 确定了 alpha、beta 和 gamma 频带中的源，并成功地分离了相同频带中的不同网络。
GED 是一种灵活的特征引导分解方法，以前已经成功应用。我们的联合频率和条件调整是一种新颖的适应，扩展了 GED 在认知电生理学中的功能。
我们通过将 csGED 应用于模拟和实证数据来证明成功的条件特异性源分离。
